Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	53.850834
daal4py_KNeighborsClassifier	0.0	3.0	30.570622
KNeighborsClassifier_kd_tree	0.0	7.0	25.276314
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	51.245386
KMeans_tall	0.0	1.0	42.475878
daal4py_KMeans_tall	0.0	1.0	19.272617
KMeans_short	0.0	0.0	23.884309
daal4py_KMeans_short	0.0	0.0	11.016962
LogisticRegression	0.0	1.0	4.300081
daal4py_LogisticRegression	0.0	0.0	57.582113
Ridge	0.0	0.0	49.973108
daal4py_Ridge	0.0	0.0	16.137633
total	0.0	33.0	25.658883

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.148	0.004	1000000	1000000	100	brute	-1	1	0.983	0.982	0.494	0.005	0.299	0.008	See
1	KNeighborsClassifier	predict	26.411	0.853	1000000	1000	100	brute	-1	1	0.983	0.982	1.993	0.033	13.252	0.481	See
2	KNeighborsClassifier	predict	0.171	0.018	1000000	1	100	brute	-1	1	0.983	0.982	0.085	0.001	2.008	0.212	See
3	KNeighborsClassifier	fit	0.147	0.003	1000000	1000000	100	brute	-1	5	0.983	0.982	0.483	0.002	0.305	0.006	See
4	KNeighborsClassifier	predict	34.575	0.000	1000000	1000	100	brute	-1	5	0.983	0.982	2.110	0.095	16.388	0.736	See
5	KNeighborsClassifier	predict	0.176	0.016	1000000	1	100	brute	-1	5	0.983	0.982	0.087	0.001	2.023	0.185	See
6	KNeighborsClassifier	fit	0.144	0.002	1000000	1000000	100	brute	-1	100	0.983	0.982	0.495	0.001	0.291	0.005	See
7	KNeighborsClassifier	predict	36.478	0.000	1000000	1000	100	brute	-1	100	0.983	0.982	2.139	0.102	17.050	0.816	See
8	KNeighborsClassifier	predict	0.194	0.017	1000000	1	100	brute	-1	100	0.983	0.982	0.089	0.003	2.178	0.210	See
9	KNeighborsClassifier	fit	0.148	0.003	1000000	1000000	100	brute	1	1	0.983	0.982	0.482	0.002	0.307	0.007	See
10	KNeighborsClassifier	predict	13.502	0.228	1000000	1000	100	brute	1	1	0.983	0.982	2.079	0.099	6.495	0.328	See
11	KNeighborsClassifier	predict	0.192	0.003	1000000	1	100	brute	1	1	0.983	0.982	0.090	0.003	2.131	0.070	See
12	KNeighborsClassifier	fit	0.147	0.003	1000000	1000000	100	brute	1	5	0.983	0.982	0.489	0.001	0.301	0.006	See
13	KNeighborsClassifier	predict	23.715	0.127	1000000	1000	100	brute	1	5	0.983	0.982	1.978	0.030	11.987	0.195	See
14	KNeighborsClassifier	predict	0.197	0.002	1000000	1	100	brute	1	5	0.983	0.982	0.084	0.000	2.331	0.024	See
15	KNeighborsClassifier	fit	0.140	0.002	1000000	1000000	100	brute	1	100	0.983	0.982	0.483	0.003	0.289	0.005	See
16	KNeighborsClassifier	predict	23.536	0.433	1000000	1000	100	brute	1	100	0.983	0.982	2.168	0.079	10.858	0.443	See
17	KNeighborsClassifier	predict	0.195	0.002	1000000	1	100	brute	1	100	0.983	0.982	0.091	0.003	2.141	0.075	See
18	KNeighborsClassifier	fit	0.060	0.001	1000000	1000000	2	brute	-1	1	0.983	0.982	0.113	0.001	0.534	0.014	See
19	KNeighborsClassifier	predict	23.413	0.137	1000000	1000	2	brute	-1	1	0.983	0.982	0.329	0.009	71.100	1.996	See
20	KNeighborsClassifier	predict	0.022	0.003	1000000	1	2	brute	-1	1	0.983	0.982	0.007	0.001	3.294	0.654	See
21	KNeighborsClassifier	fit	0.064	0.003	1000000	1000000	2	brute	-1	5	0.983	0.982	0.111	0.000	0.576	0.024	See
22	KNeighborsClassifier	predict	32.639	0.000	1000000	1000	2	brute	-1	5	0.983	0.982	0.334	0.010	97.620	2.807	See
23	KNeighborsClassifier	predict	0.026	0.001	1000000	1	2	brute	-1	5	0.983	0.982	0.006	0.000	4.080	0.270	See
24	KNeighborsClassifier	fit	0.062	0.000	1000000	1000000	2	brute	-1	100	0.983	0.982	0.109	0.005	0.575	0.025	See
25	KNeighborsClassifier	predict	33.610	0.000	1000000	1000	2	brute	-1	100	0.983	0.982	0.371	0.016	90.671	3.845	See
26	KNeighborsClassifier	predict	0.026	0.002	1000000	1	2	brute	-1	100	0.983	0.982	0.007	0.001	3.749	0.599	See
27	KNeighborsClassifier	fit	0.062	0.001	1000000	1000000	2	brute	1	1	0.983	0.982	0.108	0.003	0.571	0.018	See
28	KNeighborsClassifier	predict	10.591	0.057	1000000	1000	2	brute	1	1	0.983	0.982	0.315	0.015	33.625	1.582	See
29	KNeighborsClassifier	predict	0.016	0.000	1000000	1	2	brute	1	1	0.983	0.982	0.006	0.001	2.781	0.320	See
30	KNeighborsClassifier	fit	0.064	0.000	1000000	1000000	2	brute	1	5	0.983	0.982	0.105	0.003	0.606	0.019	See
31	KNeighborsClassifier	predict	19.702	0.261	1000000	1000	2	brute	1	5	0.983	0.982	0.296	0.005	66.537	1.345	See
32	KNeighborsClassifier	predict	0.022	0.001	1000000	1	2	brute	1	5	0.983	0.982	0.006	0.001	3.445	0.758	See
33	KNeighborsClassifier	fit	0.075	0.003	1000000	1000000	2	brute	1	100	0.983	0.982	0.103	0.002	0.723	0.028	See
34	KNeighborsClassifier	predict	19.745	0.256	1000000	1000	2	brute	1	100	0.983	0.982	0.352	0.006	56.131	1.207	See
35	KNeighborsClassifier	predict	0.021	0.001	1000000	1	2	brute	1	100	0.983	0.982	0.006	0.000	3.417	0.278	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.242	0.235	1000000	1000000	10	kd_tree	-1	1	0.979	0.986	0.792	0.019	4.092	0.312	See
1	KNeighborsClassifier_kd_tree	predict	0.483	0.012	1000000	1000	10	kd_tree	-1	1	0.979	0.986	0.118	0.011	4.103	0.388	See
2	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	1	0.979	0.986	0.000	0.000	9.761	4.050	See
3	KNeighborsClassifier_kd_tree	fit	3.117	0.126	1000000	1000000	10	kd_tree	-1	5	0.979	0.986	0.794	0.024	3.927	0.197	See
4	KNeighborsClassifier_kd_tree	predict	0.933	0.019	1000000	1000	10	kd_tree	-1	5	0.979	0.986	0.204	0.005	4.565	0.149	See
5	KNeighborsClassifier_kd_tree	predict	0.005	0.003	1000000	1	10	kd_tree	-1	5	0.979	0.986	0.000	0.000	14.856	9.838	See
6	KNeighborsClassifier_kd_tree	fit	3.140	0.207	1000000	1000000	10	kd_tree	-1	100	0.979	0.986	0.759	0.009	4.137	0.277	See
7	KNeighborsClassifier_kd_tree	predict	2.670	0.078	1000000	1000	10	kd_tree	-1	100	0.979	0.986	0.577	0.011	4.625	0.163	See
8	KNeighborsClassifier_kd_tree	predict	0.007	0.001	1000000	1	10	kd_tree	-1	100	0.979	0.986	0.001	0.000	7.961	2.615	See
9	KNeighborsClassifier_kd_tree	fit	3.004	0.094	1000000	1000000	10	kd_tree	1	1	0.979	0.986	0.839	0.037	3.579	0.192	See
10	KNeighborsClassifier_kd_tree	predict	0.765	0.026	1000000	1000	10	kd_tree	1	1	0.979	0.986	0.105	0.002	7.284	0.275	See
11	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	0.979	0.986	0.000	0.000	4.881	2.320	See
12	KNeighborsClassifier_kd_tree	fit	3.194	0.211	1000000	1000000	10	kd_tree	1	5	0.979	0.986	0.833	0.040	3.835	0.312	See
13	KNeighborsClassifier_kd_tree	predict	1.365	0.016	1000000	1000	10	kd_tree	1	5	0.979	0.986	0.199	0.001	6.847	0.085	See
14	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	1	5	0.979	0.986	0.000	0.000	4.340	2.163	See
15	KNeighborsClassifier_kd_tree	fit	3.384	0.053	1000000	1000000	10	kd_tree	1	100	0.979	0.986	0.828	0.011	4.086	0.085	See
16	KNeighborsClassifier_kd_tree	predict	4.864	0.136	1000000	1000	10	kd_tree	1	100	0.979	0.986	0.608	0.032	7.994	0.480	See
17	KNeighborsClassifier_kd_tree	predict	0.005	0.001	1000000	1	10	kd_tree	1	100	0.979	0.986	0.001	0.000	5.270	1.950	See
18	KNeighborsClassifier_kd_tree	fit	1.743	0.025	1000000	1000000	2	kd_tree	-1	1	0.979	0.986	0.504	0.013	3.458	0.102	See
19	KNeighborsClassifier_kd_tree	predict	0.031	0.001	1000000	1000	2	kd_tree	-1	1	0.979	0.986	0.001	0.000	30.166	13.628	See
20	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	1	0.979	0.986	0.000	0.000	20.994	16.563	See
21	KNeighborsClassifier_kd_tree	fit	1.655	0.043	1000000	1000000	2	kd_tree	-1	5	0.979	0.986	0.509	0.009	3.252	0.104	See
22	KNeighborsClassifier_kd_tree	predict	0.033	0.001	1000000	1000	2	kd_tree	-1	5	0.979	0.986	0.001	0.001	24.857	12.206	See
23	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	5	0.979	0.986	0.000	0.000	22.256	16.304	See
24	KNeighborsClassifier_kd_tree	fit	1.710	0.040	1000000	1000000	2	kd_tree	-1	100	0.979	0.986	0.494	0.008	3.461	0.097	See
25	KNeighborsClassifier_kd_tree	predict	0.055	0.003	1000000	1000	2	kd_tree	-1	100	0.979	0.986	0.007	0.001	7.981	1.048	See
26	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	100	0.979	0.986	0.000	0.000	21.255	15.997	See
27	KNeighborsClassifier_kd_tree	fit	1.668	0.046	1000000	1000000	2	kd_tree	1	1	0.979	0.986	0.509	0.014	3.279	0.126	See
28	KNeighborsClassifier_kd_tree	predict	0.030	0.001	1000000	1000	2	kd_tree	1	1	0.979	0.986	0.001	0.000	27.042	11.379	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	0.979	0.986	0.000	0.000	6.202	5.290	See
30	KNeighborsClassifier_kd_tree	fit	1.640	0.030	1000000	1000000	2	kd_tree	1	5	0.979	0.986	0.480	0.021	3.419	0.165	See
31	KNeighborsClassifier_kd_tree	predict	0.028	0.001	1000000	1000	2	kd_tree	1	5	0.979	0.986	0.001	0.000	21.642	8.107	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	0.979	0.986	0.000	0.000	4.799	3.049	See
33	KNeighborsClassifier_kd_tree	fit	1.636	0.074	1000000	1000000	2	kd_tree	1	100	0.979	0.986	0.508	0.029	3.220	0.235	See
34	KNeighborsClassifier_kd_tree	predict	0.060	0.001	1000000	1000	2	kd_tree	1	100	0.979	0.986	0.008	0.001	7.693	0.546	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	0.979	0.986	0.000	0.000	5.733	4.792	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.683	0.015	1000000	1000000	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.480	0.023	1.422	0.075	See
1	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.249	1.916	See
2	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.164	1.932	See
3	KMeans_tall	fit	0.574	0.031	1000000	1000000	2	full	random	30	3	1	0.0	30	0.002	30	0.002	0.403	0.018	1.425	0.101	See
4	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	1.221	0.936	See
5	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.099	1.886	See
6	KMeans_tall	fit	6.520	0.177	1000000	1000000	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	3.230	0.050	2.018	0.063	See
7	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.167	1.865	See
8	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	1.296	1.542	See
9	KMeans_tall	fit	6.192	0.187	1000000	1000000	100	full	random	30	3	1	0.0	30	0.002	30	0.002	2.869	0.027	2.158	0.068	See
10	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	2.140	1.179	See
11	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	30	0.002	30	0.002	0.000	0.000	1.783	1.302	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.369	0.047	10000	10000	2	full	k-means++	30	300	1	0.0	26	0.005	27	0.007	0.114	0.003	3.230	0.427	See
1	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	26	0.005	27	0.007	0.001	0.000	1.157	0.341	See
2	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	26	0.005	27	0.007	0.000	0.000	2.122	1.641	See
3	KMeans_short	fit	0.137	0.003	10000	10000	2	full	random	30	300	1	0.0	30	0.005	30	0.007	0.050	0.004	2.761	0.212	See
4	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	30	0.005	30	0.007	0.001	0.000	1.072	0.265	See
5	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	30	0.005	30	0.007	0.000	0.000	2.012	1.636	See
6	KMeans_short	fit	0.921	0.054	10000	10000	100	full	k-means++	30	300	1	0.0	15	0.005	24	0.007	0.459	0.041	2.006	0.216	See
7	KMeans_short	predict	0.003	0.001	10000	1000	100	full	k-means++	30	300	1	0.0	15	0.005	24	0.007	0.002	0.000	1.872	0.584	See
8	KMeans_short	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	15	0.005	24	0.007	0.000	0.000	2.298	1.489	See
9	KMeans_short	fit	0.269	0.035	10000	10000	100	full	random	30	300	1	0.0	19	0.005	30	0.007	0.208	0.058	1.295	0.399	See
10	KMeans_short	predict	0.002	0.001	10000	1000	100	full	random	30	300	1	0.0	19	0.005	30	0.007	0.001	0.000	1.635	0.807	See
11	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	19	0.005	30	0.007	0.000	0.000	2.043	1.324	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.521	0.045	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.29	11.700	0.087	0.985	0.008	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.29	0.000	0.000	0.827	0.436	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	0.29	0.000	0.000	0.346	0.345	See
3	LogisticRegression	fit	0.796	0.028	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	0.29	0.771	0.033	1.033	0.056	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	0.29	0.003	0.001	0.629	0.144	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	0.29	0.001	0.000	0.119	0.089	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	1.824	0.034	100000	100000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.985	0.027	1.853	0.062	See
1	Ridge	predict	0.001	0.000	100000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.001	0.000	0.832	0.443	See
2	Ridge	predict	0.000	0.000	100000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.598	0.652	See
3	Ridge	fit	1.178	0.027	1000000	1000000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.249	0.003	4.728	0.121	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.564	0.400	See
5	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	1.0	0.000	0.000	0.597	0.698	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}